Although blockchain can provide decentralization and traceability for FL training in edge computing, recent studies have found that blockchain still has its own security issues. For example, 51% attack is a critical security problem in blockchain platforms [1]} where a group of powerful miners can take control of the mining with more than 50% of the mining power of the network. These miners can modify block data or even prevent blocks from appending to the blockchain, which interrupts the communication among users (e.g., FL clients) and FL owners (e.g., MEC servers). Another security issue is forking attacks [2]} wherein blackdivergent blockchains are generated which results in a change in the block update rule and thus invalidates the synchronized block verification in blockchain. Such a forking attack can mislead the blockchain nodes in the FL training process. Specifically, certain nodes can use different global parameters to train the shared FL model in some iterations, leading to incorrect FL model aggregation. Other attacks such as double spending attacks and reentrancy attacks on smart contracts [3]} are issues to be solved before integrating blockchain in FL systems. The fake parameter update from an adversary is also a security bottleneck [4]} blackwhich can impersonate a certain client and broadcast fake parameters. Such an adversary may deliberately broadcast fake samples where the miners cannot recognize during the mining process which leads to the incorrectness in the model aggregation.
